The automated identification of biological structures from histopathology images is a central task in digital pathology. Given a whole slide tissue image, many applications require identification of different types of cells or other structures in the images that appear in normal tissue, tumor tissue, necrosis, lymphatic regions, and stroma. In fact, a quantitative assessment of the presence of such cells or structures in a sample is often needed to determine the impact of a particular therapy, such as the selection of particular chemotherapeutic agents for treating cancer. For example, tumor cells expressing PD-L1 (Programmed death-ligand 1) are believed to suppress immune responses through activation of the PD-1/PD-L1 pathway and data indicates that PD-L1 tumor status might be predictive for responses to PD-1- and PD-L1-directed therapies. As such, PD-L1 nucleus classification and quantification is an important task in digital pathology.
Quantitative PD-L1 tissue analysis frequently requires the detection and labeling of cells or nuclei according to type (tumor, immune, stroma, etc.) or response to PD-L1 staining. The PD-L1 biomarker may be expressed on the membrane of tumor cells and immune cells. Any biologically meaningful automated analysis of image data must first detect all the cells and staining patterns and identify them as one of (1) a PD-L1 positive immune cell; (2) a PD-L1 positive tumor cell; (3) a PD-L1 negative immune cell (a cell visible by its nucleus with no immunohistochemistry (IHC) staining; (4) a PD-L1 negative tumor cell (a cell identified by the appearance of its nucleus with no PD-L1 staining); (5) any other cell, including stroma cell, normal tissue cell, etc.; and/or (6) staining not representing a cell, including artifacts, background staining, etc.
In the context of PD-L1, analysis must not only detect cells and their IHC stain, but additionally determine and classify the reason for the presence of the IHC stain. For example, local stain uptake may be caused by a PD-L1 positive tumor cell, a PD-L1 positive immune cell, or non-target artificial staining. Moreover, immune and tumor cells may occur together in a close spatial neighborhood, with PD-L1 positive and negative cells touching each other. Indeed, in order to correctly identify a single cell, the appearance of the cell's nucleus and possible membrane staining must be assessed together with multiple cells, their appearance, and the staining pattern in their local neighborhood.
Due to the large size of a whole slide image at high magnification and the large volume of data to be processed, assessment of the images by a pathologist is problematic. Indeed, the number of cells or cell nuclei present in a whole slide tissue image is typically of the order of 104, making it difficult, if not infeasible, for a pathologist to manually perform such a task. It is therefore desirable to develop an automatic quantitation assay that is able to identify each cell or cell nucleus based on its own appearance and the appearance of cells in its local tissue context.